Neural Data Science

A Primer with MATLAB® and Python™ present important information on the emergence of the use of Python, a more general purpose option to MATLAB, the preferred computation language for scientific computing and analysis in neuroscience.

This book addresses the snake in the room by providing a beginner’s introduction to the principles of computation and data analysis in neuroscience, using both Python and MATLAB, giving readers the ability to transcend platform tribalism and enable coding versatility.

• Includes discussions of both MATLAB and Python in parallel
• Introduces the canonical data analysis cascade, standardizing the data analysis flow
• Presents tactics that strategically, tactically, and algorithmically help improve the organization of code

Students, researchers and instructors in Systems, Cognitive and Behavioral Neuroscience, and Cognitive Psychology

Part I: Foundations

Chapter 1. Philosophy

• Abstract
• What Is Data Science?
• What Is Neural Data Science?
• How Is Neural Data Science Different From Computational Neuroscience?
• Data as Seen by Data Scientists Versus Data Seen by Neural Data Scientists
• What Is a Neural Data Scientist?
• Why Do I Need to be Able to Write Computer Code?
• What Is Neural Data?
• Can We Just Add “Neuro” to the Front of Anything?
• Why Python?
• Why MATLAB?
• Why Not C/C++/R/Julia/Haskill/Java/Javascript/OCaml/Perl/Pascal/Fortran/Ruby/Groovy/Scala/etc.?
• What Is Industrial Data Science? How Is It Different From Engineering?

Chapter 2. From 0 to 0.01

• Abstract
• What Is the Goal of This Chapter?
• How Do I Get Started Coding?
• What’s the Command Line? What’s the Environment?
• How Are Python and MATLAB Different?
• How Do I Display Something on the Screen?
• How Do I Do Arithmetic in Python or MATLAB?
• How Do I Input Exponents in Python and MATLAB?
• What Is the Role of Blank Space in Writing Code, If Any?
• What Is the Order of Operations in Python and MATLAB?
• What Are Functions?
• What Are Python Packages? What Are MATLAB Toolboxes? Are These Different From Libraries?
• How Do I Get Help?
• What Are Variables?
• How Can I Access or Display What Is Contained in a Given Variable?
• What Is “ans” in MATLAB?
• What Can We Call Our Variables?
• What Is a Vector? How Do I Store a Vector in POM?
• How Do I Calculate the Sum and Mean of All Values in a Vector?
• We Need to Talk About the Echo
• How Do I Calculate the Length of a Vector?
• What Are Matrices, What Are Arrays?
• Back to Vectors: How to Vectorize a Matrix?
• What Can We Do With All of This?
• The Find Function
• Adding Matrices and Dealing With Holes in Arrays
• What Is a Normal Distribution? How Do We Draw From One, How Do We Plot One With POM?
• How Do I Plot Something More Meaningful?
• How Do I Save What I’m Working On so That I Can Load It Again Later?

Part II: Neural Data Analysis

Chapter 3. Wrangling Spike Trains

• Abstract
• Questions We Did Not Address

Chapter 4. Correlating Spike Trains

• Abstract

Chapter 5. Analog Signals

• Abstract

Chapter 6. Biophysical Modeling

• Abstract
• Biophysical Properties of Neurons
• Modeling
• Why Use Simulations?
• Why Object-Oriented Programming?
• Python Is Inherently Object-Oriented: How Does MATLAB Implement These Things?
• Creating theclass Neuron
• Modeling the Response Properties of This Neuron

Part III: Going Beyond the Data

Chapter 7. Regression

• Abstract
• Describing the Relation Between Synaptic Potentials and Spikes

Chapter 8. Dimensionality Reduction

• Abstract
• Calculating the Covariance Matrix Between Variables
• Factor Extraction as an Axis Rotation
• Determining the Number of Factors
• Interpreting the Meaning of Factors
• Determining the Factor Values of the Original Variables

Chapter 9. Classification and Clustering

• Abstract
• Predictions, Validation, and Crossvalidation
• Clustering

Chapter 10. Web Scraping

• Abstract
• What Lies Beyond 1?

Appendix A. MATLAB to Python (Table of Equivalences)

• Comments
• Blankspace
• Loops
• Exponents
• Lists and Cells
• Indexing
• Importing Packages Versus Default Packages
• Random Number Generation
• Numerical Types

Appendix B. Frequently Made Mistakes

Appendix C. Practical Considerations, Technical Issues, Tips and Tricks

• Package Installation
• Python List Comprehensions
• Python Lists Versus Numpy Arrays
• Text Editors, The Command Line, How to Go between Sublime and the Terminal
• Python on Windows
• Jupyter: Using It and Its Great Functions
• The Biggest Differences Between Python 2 and 3
• Conventions in Python
• MATLAB Tips and Tricks
• Vectorization
• Practical Considerations

Glossary (Including Additional Python and MATLAB Packages and Examples)